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With the development of the modern society, mind control applied to both therecovery of disabled individuals and auxiliary control of normal people hasobtained great attention in numerous researches. In our research, we attempt torecognize the brain waves of left and right hand movement imagery with portableelectroencephalographs. Considering the inconvenience of wearing traditionalmultiple-electrode electroencephalographs, we choose Muse to collect data whichis a portable headband launched lately with a number of useful functions andchannels and it is much easier for the public to use. Additionally, previousresearches generally focused on discrimination of EEG of left and right handmovement imagery by using data from C3 and C4 electrodes which locate on thetop of the head. However, we choose the gamma wave channels of F7 and F8 andobtain data when subjects imagine their left or right hand to move with theireyeballs rotated in the corresponding direction. With the help of the CommonSpace Pattern algorithm to extract features of brain waves between left andright hand movement imagery, we make use of the Support Vector Machine toclassify different brain waves. Traditionally, the accuracy rate ofclassification was approximately 90% using the EEG data from C3 and C4electrode poles; however, the accuracy rate reaches 95.1% by using the gammawave data from F7 and F8 in our experiment. Finally, we design a plane programin Python where a plane can be controlled to go left or right when usersimagine their left or right hand to move. 8 subjects are tested and all of themcan control the plane flexibly which reveals that our model can be applied tocontrol hardware which is useful for disabled individuals and normal people.

Recognition of Brain Waves of Left and Right Hand Movement Imagery with Portable Electroencephalographs